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2020 – today
- 2025
[c6]Rana Muhammad Shahroz, Pingzhi Li, Sukwon Yun, Zhenyu Wang, Shahriar Nirjon, Chau-Wai Wong, Tianlong Chen:
PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches. ICLR 2025- [c5]Mohan Zhang, Pingzhi Li, Jie Peng, Mufan Qiu, Tianlong Chen:
Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design. NAACL (Long Papers) 2025: 6815-6825 - [i15]Haoran Wang, Pingzhi Li, Min Chen, Jinglei Cheng, Junyu Liu, Tianlong Chen:
GroverGPT: A Large Language Model with 8 Billion Parameters for Quantum Searching. CoRR abs/2501.00135 (2025) - [i14]Mingjia Shi, Ruihan Lin, Xuxi Chen, Yuhao Zhou, Zezhen Ding, Pingzhi Li, Tong Wang, Kai Wang, Zhangyang Wang, Jiheng Zhang, Tianlong Chen:
Make Optimization Once and for All with Fine-grained Guidance. CoRR abs/2503.11462 (2025) - [i13]Rana Muhammad Shahroz Khan, Dongwen Tang, Pingzhi Li, Kai Wang, Tianlong Chen:
ORAL: Prompting Your Large-Scale LoRAs via Conditional Recurrent Diffusion. CoRR abs/2503.24354 (2025) - [i12]Mohan Zhang, Pingzhi Li, Jie Peng, Mufan Qiu, Tianlong Chen:
Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design. CoRR abs/2504.01337 (2025) - [i11]Ajay Jaiswal, Jianyu Wang, Yixiao Li, Pingzhi Li, Tianlong Chen, Zhangyang Wang, Chong Wang, Ruoming Pang, Xianzhi Du:
Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations. CoRR abs/2504.05586 (2025) - 2024
- [c4]Pingzhi Li, Zhenyu Zhang, Prateek Yadav, Yi-Lin Sung, Yu Cheng, Mohit Bansal, Tianlong Chen:
Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy. ICLR 2024 - [c3]Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, Tianlong Chen:
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark. ICML 2024 - [c2]Xinyu Zhao, Guoheng Sun, Ruisi Cai, Yukun Zhou, Pingzhi Li, Peihao Wang, Bowen Tan, Yexiao He, Li Chen, Yi Liang, Beidi Chen, Binhang Yuan, Hongyi Wang, Ang Li, Zhangyang Wang, Tianlong Chen:
Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild. NeurIPS 2024 - [c1]Pingzhi Li, Tianlong Chen, Junyu Liu
:
Enhancing Quantum Security over Federated Learning via Post-Quantum Cryptography. TPS-ISA 2024: 499-505 - [i10]Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, Tianlong Chen:
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark. CoRR abs/2402.11592 (2024) - [i9]Tiejin Chen, Longchao Da, Huixue Zhou, Pingzhi Li, Kaixiong Zhou, Tianlong Chen, Hua Wei:
Privacy-preserving Fine-tuning of Large Language Models through Flatness. CoRR abs/2403.04124 (2024) - [i8]Pingzhi Li, Junyu Liu, Hanrui Wang, Tianlong Chen:
Hybrid Quantum-Classical Scheduling for Accelerating Neural Network Training with Newton's Gradient Descent. CoRR abs/2405.00252 (2024) - [i7]Pingzhi Li, Xiaolong Jin, Yu Cheng, Tianlong Chen:
Examining Post-Training Quantization for Mixture-of-Experts: A Benchmark. CoRR abs/2406.08155 (2024) - [i6]Pingzhi Li, Tianlong Chen, Junyu Liu:
Enhancing Quantum Security over Federated Learning via Post-Quantum Cryptography. CoRR abs/2409.04637 (2024) - [i5]Xinyu Zhao, Guoheng Sun, Ruisi Cai, Yukun Zhou, Pingzhi Li, Peihao Wang, Bowen Tan, Yexiao He, Li Chen, Yi Liang, Beidi Chen, Binhang Yuan, Hongyi Wang, Ang Li, Zhangyang Wang, Tianlong Chen:
Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild. CoRR abs/2410.05357 (2024) - [i4]Pingzhi Li, Prateek Yadav, Jaehong Yoon, Jie Peng, Yi-Lin Sung, Mohit Bansal, Tianlong Chen:
Glider: Global and Local Instruction-Driven Expert Router. CoRR abs/2410.07172 (2024) - [i3]Rana Muhammad Shahroz Khan, Pingzhi Li, Sukwon Yun, Zhenyu Wang, Shahriar Nirjon, Chau-Wai Wong, Tianlong Chen:
PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches. CoRR abs/2410.10870 (2024) - [i2]Shuqing Luo, Jie Peng, Pingzhi Li, Tianlong Chen:
HEXA-MoE: Efficient and Heterogeneous-aware MoE Acceleration with ZERO Computation Redundancy. CoRR abs/2411.01288 (2024) - 2023
- [i1]Pingzhi Li, Zhenyu Zhang, Prateek Yadav, Yi-Lin Sung, Yu Cheng, Mohit Bansal, Tianlong Chen:
Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy. CoRR abs/2310.01334 (2023)
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